PSY 406 Final Study Guide

What is a concept? What is the difference between an artificial concept and a natural concept? Which of these would someone be likely to be successful using rules/definitions to determine category membership?

A concept is a mental representation of a class or individual that captures the meaning of objects, events, and abstract ideas. Artificial concepts have clear rules that determine category membership (like mathematical shapes), while natural concepts have fuzzy boundaries and varied examples (like "furniture"). Rules/definitions work effectively for artificial concepts because they have necessary and sufficient conditions without exceptions, whereas natural concepts resist simple definition.

What is meant by family resemblance for membership in a natural category?

Family resemblance refers to how members of natural categories share overlapping features without any single feature being necessary for membership. Like family members who may share various combinations of traits (nose shape, eye color, etc.), category members share similarities, but no single defining feature must be present in all members. For example, chairs might have backs, seats, legs, and support weight, but bean bag chairs lack some features yet remain chairs through overall resemblance to the category.

What would an incremental learning approach predict about performance in learning a category over time? What would a hypothesis testing approach predict about performance in learning a category over time? 

An incremental learning approach predicts gradual improvement in category learning, with performance steadily increasing over time as associations strengthen with each exposure. When correctly categorizing items, associations increase; when incorrectly categorizing, associations decrease.

A hypothesis testing approach predicts that performance will remain at chance level until the correct rule is discovered, at which point performance jumps to perfect or near-perfect accuracy. This approach shows no improvement until the "aha" moment when the correct hypothesis is formed.

What is a prototype view of categorization? What is an exemplar view? What do they predict about categorization, and what evidence supports each view?

The prototype view proposes that we categorize by comparing new items to a single prototype (either the best example or central tendency/average) of the category. It predicts faster and more accurate classification of prototypical items even if never seen before.

The exemplar view suggests we store and compare new items to multiple specific examples from a category. It predicts better performance with previously encountered instances and sensitivity to specific variations within categories.

Evidence supporting prototype view: Prototypes are categorized more rapidly even when never seen before, and prototype recognition shows little forgetting after delays. Evidence supporting exemplar view: People are sensitive to specific instances and to category variability, and can handle exceptions and edge cases within categories.

What does the embodied / grounded cognition approach predict about how we represent categories? 

The embodied/grounded cognition approach predicts that category representations activate the same neural regions used during actual experience with category members. Concepts function as "simulators" that activate sensory, motor, and emotional systems associated with experiencing the concept. For example, reading action words like "kick" activates motor regions for leg movement, and object size affects corresponding motor actions (small objects prime small motor movements). The "hub and spoke" model suggests the anterior temporal lobe coordinates conceptual knowledge across different modalities.

What is the "hub and spoke" model of embodied cognition?

The "hub and spoke" model of embodied cognition proposes that conceptual knowledge is organized with the anterior temporal lobe acting as a central hub that coordinates information across different modalities (the spokes). The hub integrates sensory, motor, and emotional information from distributed brain regions. When this hub is disrupted through techniques like transcranial magnetic stimulation, performance on conceptual tasks is severely impaired, while disrupting individual spokes only affects specific types of judgments. This model explains how we can access conceptual knowledge through multiple pathways and how damage to the hub can cause widespread conceptual impairments.

What is meant by language as a joint activity? What is common ground, and how do people establish common ground when communicating?

Language as a joint activity refers to communication being a collaborative process where two or more people work together to achieve shared understanding. Common ground is the knowledge, beliefs, and assumptions shared between speakers, including background knowledge, current conversational context, and shared experiences. People establish common ground through a process called "grounding" which involves presentation (offering information) and acceptance phases (confirming understanding). As common ground increases through repeated interactions, communication becomes more efficient, requiring fewer words and resulting in better performance, as demonstrated in director-matcher card arrangement experiments.

What does learning of common ground among amnesiacs and healthy controls tell us about the nature of common ground in memory?

Studies with amnesiacs and healthy controls reveal that common ground depends on both explicit and implicit memory systems. Even amnesiacs with impaired explicit memory showed improved communication efficiency over repeated interactions, suggesting common ground partially relies on implicit memory processes. However, healthy controls with intact explicit memory showed additional benefits, indicating common ground utilizes both memory systems. This explains how we can build common ground with someone even when we can't explicitly recall all details of previous interactions.

What is a phoneme? Morpheme? Word?

A phoneme is the smallest unit of sound in language that can distinguish meaning (e.g., /b/, /p/, /t/ in "bit"). A morpheme is the smallest unit that carries meaning (e.g., "table" is one morpheme, while "bark-ing" has two). A word is a combination of one or more morphemes that functions as a basic unit of syntax and meaning in language.

Do pauses between words help us segment speech when we hear it? 

Pauses between words don't reliably help us segment speech, as natural speech rarely contains consistent pauses between words. Instead, we rely on multiple cues including stress patterns, pitch variations, and crucially, context and statistical probabilities of syllable transitions. Context plays a vital role in identifying ambiguous sounds or words within sentences.

How does context in a sentence help us understand words or phonemes? How does context of a word help us identify letters? What is the word frequency effect? How are these examples of top-down processing?

Context in sentences helps us understand words through top-down processing. In the phonemic restoration effect, listeners "hear" sounds covered by noise when context suggests what should be there (e.g., hearing "wave" in "There was time to *ave our friend goodbye"). Similarly, the word superiority effect shows letters are identified more accurately when embedded in words than in isolation, demonstrating how word context aids letter identification. The word frequency effect shows we process common words faster than rare ones because our prior experience creates expectations. These are all examples of top-down processing where higher-level knowledge (sentence meaning, word recognition, frequency statistics) influences lower-level perception.

How does statistical learning of common syllabus transitions help us segment speech into words?

Statistical learning helps us segment speech by tracking probabilities of syllable transitions. After exposure to a stream of speech, we detect which syllables commonly occur together (high transitional probability) versus which rarely occur together (low transitional probability). Even 8-month-old infants demonstrate this ability - they look longer at unexpected syllable combinations compared to expected ones after brief exposure to speech streams. For example, after hearing "golabu" repeatedly, infants learn these syllables belong together, and they notice when the sequence is broken. This statistical learning mechanism helps us identify word boundaries without relying on pauses, which are often absent in natural speech.

What is an anaphoric inference? Instrument inference? Causal inference?

An anaphoric inference involves determining what a pronoun or noun phrase refers to from earlier in the text. For example, in "Lisa's poodle Riffifi won the dog show. She eats a specialized diet," readers must infer that "she" refers to the dog Riffifi, not Lisa. This connects elements across sentences.

An instrument inference involves deducing the means or tool used to accomplish an action when not explicitly stated. For example, reading "John cut the birthday cake" leads to inferring he used a knife, though no specific instrument was mentioned.

A causal inference involves drawing conclusions about cause-effect relationships between events. For example, after reading "The actress fell from the 14th floor," readers might infer she died, even though this outcome wasn't stated. Studies show people who focus on contextual elaboration recognize words like "DEAD" faster after such sentences, demonstrating how we automatically generate causal inferences.

What is syntax? How does it contribute to our understanding and interpreting of meaning?

Syntax refers to the rules for combining words into grammatically correct sentences. It contributes significantly to meaning by establishing relationships between words that determine "who did what to whom." Even when individual words are understood, improper syntax can obscure meaning (e.g., "Close election the was very").

Syntax guides interpretation through structural cues that reduce ambiguity. For example, in "Put the apple on the towel in the box," the ambiguous prepositional phrase causes momentary confusion about whether "on the towel" describes the apple's location or destination. However, adding "that's" ("Put the apple that's on the towel in the box") clarifies the syntax and eliminates confusion, as shown by eye tracking studies where participants fixate less on incorrect locations.

Research shows we maintain syntactic structures between sentences, making processing easier when similar structures are repeated. Switching between different syntactic structures requires more cognitive effort and slows reading time. Interestingly, visual-spatial experiences can prime syntactic processing, suggesting syntactic representations aren't purely linguistic but may involve spatial components, demonstrating how deeply syntax influences our understanding of language.

What are the differences in active vs. passive construction in problem solving? Do you slow down to hear an unfamiliar sentence?

Active vs. passive construction refers to how sentences are structured syntactically. Participants are primed by the form of previous sentences: subject-first (active) or object-first (passive). The research by Bock et al. (1986) shows that it does slow you down to hear a sentence you're not used to hearing.

The notes indicate that syntactic structure is represented both linguistically and spatially. Allen et al. (2010) conducted experiments showing that reading times are reduced when sentences are presented in blocks of the same structure, and reading is slower when switching between different structures. This suggests we maintain syntactic structure between sentences, and switching becomes cognitively costly, requiring more mental effort.

The second experiment by Allen et al. (2010) demonstrated that participants performed better on reading tasks when the syntactic structure matched spatial information they had previously viewed. This suggests overlap between how we represent sentence structure and physical characteristics we perceive spatially in our environment. The notes specifically mention that "watching something visual primed the way you hear the sentence."

What is a problem? What is creative problem solving?

A problem is defined as "a situation in which there is an obstacle between a present state and a goal, and it is not immediately obvious how to get around the obstacle." It involves three components: current state, desired state, and operations to overcome obstacles.

Problem solving is defined as the "application of ideas, skills, or information to achieve a solution to a problem" (to close the gap between current and desired states).

Creative problem solving involves overcoming fixedness and generating novel solutions. The notes discuss several aspects of creativity:

1. There can be a benefit to not having existing examples, as this can increase creativity.

2. Making your environment varying and stimulating provides lots of examples that can inspire creativity.

3. Overcoming functional fixedness (being stuck in seeing an object having only one particular use) is key to creative problem solving.

4. Creative problem solving involves both associative (divergent) thinking and effortful processing to inhibit prior associations.

5. Creativity emerges from the interaction between mind-wandering/memory retrieval (default network) and top-down executive functioning (control network).

What is trial and error problem solving? When are these types not always useful and when is it useful? Please explain an experiment corresponding with trial and error. What is Thorndike’s law of effect?

Trial and error problem solving involves trying different solutions until finding one that works. The benefit of trial and error is that it doesn't require a lot of information or thinking to get to the desired outcome—it just requires behavior or "doing stuff until actions happen."

Edward Thorndike studied trial and error through his experiments with cats in puzzle boxes. His law of effect states that behaviors that lead to satisfying consequences are "stamped in" or reinforced, while behaviors leading to unsatisfying consequences are weakened. This is how animals learn via reinforcement.

When trial and error is useful:

• When you're one step away from solving the problem

• When you don't need a lot of information going into the problem

• When simple actions can lead to solutions

When trial and error is not always useful:

• When problems have lots of possible actions (like a Rubik's cube)

• When there are many intermediate states between the current state and goal state

• When problems have sub-goals that must be achieved before reaching the final goal

• In complex social situations with almost infinite options

Thorndike's experiment with cats in puzzle boxes, where the cats had to perform specific actions to escape. This was challenging when the cats needed to perform multiple steps before being able to open the door, demonstrating the limitation of trial and error in multi-step problems.

What is insight problem solving? What are the three phases of insight. What is a sudden solution? What is restructuring? What is the neural activity regarding suppression or irrelevant information? For the insight solution, what areas are higher before insight solution? When is insight problem solving not always useful and when is it useful? Please explain an experiment corresponding with insight problem solving.

Insight problem solving involves the sudden realization of a problem's solution. It was studied by Gestalt psychologists of the early 20th century who believed that organization of the whole mind comes together to have a realization in the moment.

Three phases of insight:

1. Initial search through wrong representation - You are "cold" because you're not thinking about the problem the right way

2. Impasse - Being stuck and trying to figure out a different way to solve the problem

3. Restructuring - Thinking about the problem differently, which leads to a solution

The representation phase involves trying to understand the problem (features and operations), then attempting to solve through a faulty problem space, leading to impasse. At impasse, you either cannot come up with a solution or need to try a different structure. If restructuring still leaves you stuck, you return to impasse until you succeed.

A sudden solution occurs when the right restructuring is found, making the answer suddenly clear.

Restructuring means thinking about the problem in a different way or from a new perspective.

Regarding neural activity, the notes mention that insight requires suppressing competing wrong answers or solutions, and inhibiting irrelevant information. The notes don't specifically mention which areas are higher before insight solution, though they do indicate a positive correlation between insight and mind wandering.

When is insight problem solving not always useful and when is it useful? Please explain an experiment corresponding with insight problem solving.

When insight problem solving is useful:

• For problems requiring creative thinking and novel perspectives

• When conventional approaches fail

• After taking a break from a problem (mind wandering)

When insight problem solving is not always useful:

• When it's hard to study due to its unpredictable nature

• Not everything we solve comes as a sudden solution

An experiment corresponding to insight problem solving was conducted by Metcalfe and Wiebe (1987). They compared how people solved algebra problems versus insight problems (like riddles). For insight problems, participants reported feeling "cold, cold, cold, cold" until immediately having the correct answer. This contrasts with algebra problems where progress is more incremental. This experiment demonstrated the sudden nature of insight solutions compared to more analytical problem-solving approaches.

What does it mean to apply an algorithm in problem solving? When is algorithm problem solving not always useful and when is it useful? Please explain an experiment corresponding with algorithmic problem solving. What is problem space theory? Explain goal directedness, planning a sequence of operations, implementing operation, and subgoal decompositions.

Applying an algorithm in problem solving means using a sequence of actions to achieve a desired outcome. It requires much more knowledge about a problem than trial and error.

Characteristics of algorithmic problem solving include:

1. Goal directedness - Having a clear end state in mind

2. Planning a sequence of operations - Mapping out steps to take

3. Implementing operations - Carrying out the planned steps

4. Subgoal decomposition - Breaking down complex goals into smaller, manageable subgoals

Problem space theory involves mapping the entire journey from initial state to goal state, including all states in between. It includes operators (actions to move between states), such as moving discs in the Tower of Hanoi puzzle.

When algorithmic problem solving is useful:

• When clear steps can be identified

• When problems can be broken down into subgoals

• When planning ahead is beneficial

When algorithmic problem solving is not always useful:

• When fully planning becomes exponentially hard

• When it's impossible to do a full search of the problem space

• When simpler shortcuts or heuristics would be more efficient

Regarding experiments, the Tower of Hanoi puzzle is mentioned as an example of algorithmic problem solving. In this experiment, participants must move rings from one peg to another following specific rules. The notes also mention neuroimaging evidence for subgoal reward, showing activity in the anterior cingulate cortex and insula when there's an unexpected reduction in time to reach a subgoal, even when overall time was maintained.

The notes explain that goal directedness refers to having a clear target outcome, planning a sequence involves mapping steps to take before implementation, implementing operations means carrying out the planned steps, and subgoal decomposition involves breaking down complex goals into manageable parts to make the problem space more manageable and search more feasible.

What is a heuristic? What are the negatives of heuristic thinking and what are the benefits? How does this relate to subgoal reward? What does the neuroimaging look like? What is the differenc reduction heuristic?

A heuristic is a practical, fast, and easy mental shortcut for judgment, decision-making, and problem-solving. The term comes from Greek "heuriskein" meaning "to find" (same as eureka).

Benefits of heuristics:

• Save effort and time

• Allow judgments without complete information

• Enable faster problem-solving ("fast and frugal")

Negatives of heuristics:

• Often lead to systematic errors in judgment

• Do not always produce optimal outcomes like algorithmic thinking

• Can be biased and irrational

Subgoal reward relates to heuristics through the way we decompose problems. Neuroimaging evidence shows activity in the anterior cingulate cortex and insula when there's an unexpected reduction in time to reach a subgoal, even when overall time was maintained. This suggests our brain rewards progress toward subgoals, reinforcing heuristic approaches.

The difference reduction heuristic (also called hill climbing) is where people prefer actions that lead to the biggest similarity between current and goal state. People avoid taking steps that move them away from the final state, even if those steps might ultimately lead to a better solution.

What does it mean to use an analogy in problem solving? When is using an analogy problem solving not always useful and when is it useful? Please explain an experiment corresponding with using an analogy problem solving. What are three stages of using an analogy? Explain low and high structural similarity and how it corresponds with low and high surface similarity in regards to analogy problem solving.

Using an analogy in problem solving means applying knowledge from a familiar domain (source) to help solve a problem in an unfamiliar domain (target). It's based on the principle that similar problems tend to have similar solutions.

When analogy is useful:

• When facing new problems with similar structures to known problems

• When structural relationships between problems are clear

• For experts who can focus on structural rather than surface features

When analogy is not always useful:

• When surface similarity is high but structural similarity is low (misleading)

• When people get caught in a "mental set" and try to force an inappropriate analogy

Three stages of using an analogy:

1. Noticing the analogy (recognizing potential similarity)

2. Mapping the source problem to the target problem

3. Applying the mapping to generate a solution

Structural similarity refers to comparable relationships between objects in different domains. Surface similarity refers to superficial features that may look similar but function differently.

Combinations of structural and surface similarity:

• High structural, high surface: Easiest to notice and apply (e.g., comparing similar physics problems)

• High structural, low surface: Powerful but hard to notice (e.g., comparing atom to solar system)

• Low structural, high surface: Misleading and can hurt performance

• Low structural, low surface: Unlikely to be noticed or applied

An experiment showed that people transfer methods from source problems, but surface similarity without structural similarity hurts performance, as people in a "mental set" group continued trying to use inappropriate methods.

What is creativity? What are creativity’s implication with and without an example condition? What is functional fixedness? Give examples. Explain MRI results for participants in high-constraint and low-constraint conditions and what it means neurologically and for creativity. How do you overcome fixedness?

Creativity is the ability to generate novel and useful ideas or solutions. It involves divergent thinking and overcoming established mental patterns.

Creativity implications with/without examples:

• Without existing examples: increased chance of novel solutions

• With varied examples: stimulating environment can inspire creativity

• With fixed examples: can lead to fixation on conventional approaches

Functional fixedness is the tendency to see objects as having only one particular use, limiting creative problem-solving. Examples include:

• Seeing a paperclip only as a paper fastener rather than a wire tool

• Viewing a shoe only for foot protection rather than as a hammer or doorstop

MRI results comparing high-constraint vs. low-constraint conditions:

• High-constraint condition (strong prior associations): More activity in both default network and control network, particularly LPFC (lateral prefrontal cortex) for inhibition

• Low-constraint condition: Generated more creative uses with greater "semantic distance" between cue and responses

• Result: Stronger inhibition from control network to default network in high-constraint condition

Neurologically, this shows creativity emerges from interaction between:

• Mind-wandering/memory retrieval (default network)

• Top-down executive functioning (control network)

Ways to overcome fixedness:

• Actively work against prior associations using inhibitory processes

• Use novel objects without established functions

• Change problem representation

• Take breaks to allow mind-wandering

What are ways to improve creativity and problem solving? What is decision making? How do we evaluate whether something is true?

Ways to improve creativity and problem solving:

1. Increase domain knowledge

2. Produce many dissimilar ideas when brainstorming

3. Resist fixation of function and mental set

4. Change problem representation

5. Develop subgoals

6. Work backward & look forward

7. Look for analogies

8. Practice problem solving regularly

Decision making is the process of selecting between multiple options based on judgment of their value or utility.

What is deductive reasoning? What is inductive reasoning? Syllogism?

1. Deductive reasoning: Theory-based, top-down inference that produces conclusions that are either true or false. It starts with general principles and derives specific conclusions.

2. Inductive reasoning: Data-based, bottom-up inference that produces probabilistic conclusions. It starts with specific observations and develops general predictions.

A syllogism is a form of deductive reasoning with:

• Major premise (e.g., All men are mortal)

• Minor premise (e.g., Socrates is a man)

• Conclusion (e.g., Therefore, Socrates is mortal)

A syllogism is a form of logical argument with two premises and a conclusion. It's valid if the conclusion logically follows from the premises, regardless of whether the premises or conclusion are factually true.

Explain syllogism? Explain it in the context of belief bias. What is the belief-logic conflict? What happened when participants underwent an fMRI while judging syllogisms? What part of the brain was incorporated?

A syllogism is a form of logical argument with two premises and a conclusion. It's valid if the conclusion logically follows from the premises, regardless of whether the premises or conclusion are factually true.

Belief bias in syllogisms refers to our tendency to judge validity based on the believability of the conclusion rather than its logical structure. For example, people more readily accept "All students are tired, some tired people are irritable. Therefore, some students are irritable" even if it's invalid, because the conclusion seems believable.

Belief-logic conflict occurs when:

• Valid syllogisms have unbelievable conclusions

• Invalid syllogisms have believable conclusions

These conflicts create tension between our logical reasoning and intuitive judgment.

When participants underwent fMRI while judging syllogisms, the right inferior frontal gyrus activated on correct trials with unbelievable conclusions. This brain region is associated with inhibition of prepotent responses and top-down effortful processing. The interpretation is that people must inhibit making judgments based solely on the conclusion's believability to evaluate logical validity properly.

Explain a mental model and what it means for syllogisms. Does deductive reasoning always follow logic? What is a conditional syllogism? What is affirming the antecedent? Denying the consequent?

A mental model is a cognitive representation of the premises in reasoning. For syllogisms, it involves:

• Building a "model" of the premises

• Looking for exceptions

• If no exceptions are found, the syllogism is considered true

Mental models aren't necessarily visual but can help people visualize relationships. Studies with artists, beekeepers, and chemists showed participants did better when they could visualize the syllogism.

Deductive reasoning does not always follow logic. People often deviate from optimal behavior, especially when logical conclusions contradict previous knowledge or understanding.

A conditional syllogism uses "if...then" statements:

• If p then q

Four types of conditional reasoning:

1. Affirming the antecedent (valid): If p then q; p is true; therefore, q is true Example: "If it rains, the ground is wet. It is raining. Therefore, the ground is wet." (97% get correct)

2. Denying the consequent (valid): If p then q; q is false; therefore, p is false Example: "The ground is not wet. Therefore, it is not raining." (60% get correct)

3. Affirming the consequent (invalid): If p then q; q is true; therefore, p is true Example: "The ground is wet. Therefore, it is raining." (Only 40% get correct)

4. Denying the antecedent (invalid): If p then q; p is false; therefore, q is false Example: "It is not raining. Therefore, the ground is not wet." (Only 40% get correct)

People find familiar contexts and social norms easier to reason about correctly.

What strengthens inductive reasoning? Explain the availability heuristic and the representativeness heuristic. What is base rate neglect and do we use probabilistic thinking when making decisions?

Factors that strengthen inductive reasoning:

1. Frequency: Conclusions from many examples are more convincing than from few

2. Representativeness: Generalizations are more justified when examples and target cases are similar

The availability heuristic is when we estimate the frequency of an event by assessing how easily it comes to mind. It can be distorted by:

• Familiarity (overestimating based on what we know)

• Salience (being influenced by flashy or dramatic examples)

• Recency (being biased by what just happened)

Example: People may think New York is more dangerous than statistically safer places because they can easily recall dramatic crime stories from media.

The representativeness heuristic judges the likelihood that case A belongs to class B based on how well A resembles typical members of B. It's driven by what "seems like" rather than actual probabilities.

Example: In the "Linda problem," people judge "Linda is a bank teller and feminist" as more probable than "Linda is a bank teller" because the fuller description matches her description better, even though this violates probability laws.

Base rate neglect is when people ignore the prior probability (base rate) of something occurring in favor of representative information. We should use Bayesian thinking that incorporates prior probabilities, but often fail to do so.

People generally don't use optimal probabilistic thinking when making decisions, instead relying on heuristics that save time but introduce systematic biases.

What happens when we make an estimation after being presented with an initial random number? What does this have to do with heuristics?

When we make an estimation after being presented with an initial random number, we experience anchoring and adjustment. This heuristic causes our final estimate to be biased toward the initial value (anchor), even when that value is known to be arbitrary or random.

In a famous experiment, participants were shown a wheel of fortune that stopped at either 10 or 65, then asked to estimate the percentage of African nations in the UN:

• Those who saw the wheel stop at 10 gave an average estimate of 25%

• Those who saw the wheel stop at 65 gave an average estimate of 45%

This demonstrates that:

1. We use the initial value as a starting point (anchor)

2. We adjust away from this value when making our estimate

3. The adjustment is typically insufficient (doesn't move far enough from the initial anchor)

This heuristic relates to the broader concept of cognitive shortcuts that save mental effort but introduce systematic biases. Even though the wheel numbers were obviously random and unrelated to African nations, they still significantly influenced estimates, showing how our judgment can be unconsciously biased by irrelevant information.

When there are multiple options, how do we make decisions? What is the expected utility theory? Explain utility theory in terms of rationality, transitivity, and consistence? Explain the monkey Kool-Aid study and the implications of the study with the OFC and utility theory. Does correlation equal causation? What are the results and implications of the alternative study with 2 conditions and a choice?

When facing multiple options, we theoretically should choose based on expected utility—a measure of usefulness, satisfaction, or pleasure derived from each option.

Expected utility theory proposes that rational decision-makers should:

• Always prefer options with highest utility (rationality)

• If they prefer A over B and B over C, they should prefer A over C (transitivity)

• If they prefer A over B now, they should prefer A over B later (consistency)

Since utility can't be measured directly, economists use revealed preference—if you choose apples over oranges, apples must have higher utility for you.

In the monkey Kool-Aid study, researchers measured neural activity in the orbitofrontal cortex (OFC) while monkeys chose between different amounts of water and Kool-Aid. They found:

• Neural activity in OFC corresponded to subjective value of options

• The point of indifference (where 4 water = 1 Kool-Aid) showed equal OFC activity

In a follow-up study with grape juice vs. peppermint tea, researchers disrupted OFC activity:

• Condition 1: Disruption during offer 1 (grape juice)

• Condition 2: Disruption during offer 2 (peppermint tea)

• Result: Increased preference for the non-disrupted option in both conditions

Implications: Electric current reduced utility-based decision making, suggesting OFC is crucial for assigning subjective value to options.

Correlation doesn't equal causation, but this experiment used direct manipulation (disrupting OFC), providing stronger evidence for causal relationships.

Human behavior often violates expected utility theory through effects like:

• Decoy effect: Adding irrelevant third options changes preferences

• Endowment effect: Valuing owned items more highly

• Loss aversion: Preferring to avoid losses over acquiring equivalent gains

What is the decoy effect, endowment effect, prospect theory, optimism bias? Explain studies that correlate with each and the benefits and problems of each effect/bias.

The decoy effect is a violation of consistency in decision-making where a third irrelevant option changes preferences between two original options. This occurs when adding a clearly inferior option makes one of the original options seem more attractive.

The endowment effect describes how people value goods more highly when they own them. In studies, participants assigned as "owners" of a mug demanded significantly higher selling prices than what "non-owners" were willing to pay for the same mug, demonstrating an irrational increase in subjective value based on ownership.

Prospect theory explains decision-making biases by recognizing that:

• People experience "diminishing returns" for gains and losses

• Losses weigh heavier than equivalent gains

• People tend to avoid risk when framed as gains but seek risk when avoiding losses In studies, when identical outcomes are framed differently (like "saving 200 lives" vs. "400 people will die"), people choose different options despite identical outcomes.

Optimism bias relates to how unexpected positive events (like sports team wins) affect mood and risk-taking behavior. Studies have shown that when sports teams unexpectedly win, people buy more lottery tickets, suggesting mood changes from unexpected wins influence what actions people prefer. These biases and effects reflect System 1 (intuitive, fast) thinking rather than System 2 (deliberative, slow) thinking.

What is cognitive control? What is an response interference task? Explain it in terms of the Flanker task, Stroop task, and the Simon task. What does it mean behaviorally if the tasks are incongruent/incompatible or congruent/compatible?

Cognitive control is the ability to use cognition to flexibly guide behavior to achieve goals or intentions while overcoming compelling response tendencies. It incorporates both suppression and enhancement of different information based on relevance.

Response interference tasks are experimental paradigms that require cognitive control to resolve conflict between competing responses:

Stroop task: Participants must name the color of text while ignoring the word itself.

• Goal: Name color

• Compelling response: Read word

• Congruent: Word and color match (BLUE in blue ink) - faster responses

• Incongruent: Word and color conflict (BLUE in red ink) - slower responses due to interference

Flanker task: Participants identify the direction of a central arrow surrounded by other arrows.

• Goal: Focus on middle arrow

• Compelling response: Process all arrows

• Congruent: All arrows point same direction (→→→→→) - faster responses

• Incongruent: Middle arrow differs from surrounding arrows (→→←→→) - slower responses

Simon task: Participants press buttons based on color, ignoring position.

• Goal: Focus on color (e.g., blue=left button, green=right button)

• Compelling response: Respond on same side as stimulus

• Compatible: Color and position match (blue stimulus on left) - faster responses

• Incompatible: Color and position conflict (blue stimulus on right) - slower responses

When tasks are incongruent/incompatible, response times are slower and accuracy decreases because cognitive control must resolve conflict between goal-relevant and distracting information.

What is guided activation theory? What is proactive and reactive control? Use examples to explain them.

Guided activation theory of cognitive control proposes that the prefrontal cortex accounts for goals, rules, and context to activate appropriate responses. It functions as an intermediary between stimulus perception and motor responses, like changing tracks on a railroad. The prefrontal cortex (particularly DLPFC) resolves potential conflict while the anterior cingulate cortex (ACC) detects conflicting information.

Proactive control is anticipatory, preparatory control engaged before control is needed. It involves:

• Planning and goal maintenance

• Stimulus-independent preparation

• Example: Deliberately maintaining your shopping list in mind throughout the day because you know you need to go shopping after work

Reactive control is activated in the moment when control is needed. It involves:

• Responding to interference as it occurs

• Requires perceiving an external cue

• Example: Forgetting about shopping until seeing a grocery list on your desk, which triggers the memory

In experiments with different proportions of congruent vs. incongruent Stroop trials, people employ proactive control when they anticipate more incongruent trials ("I need to focus on the color") versus reactive control when they don't anticipate needing control until the conflict appears.

What is task switching? What is task inertia and what do you need to do internally to switch tasks?

Task switching is the cognitive process of changing from one task to another, which requires cognitive control to activate a new task set while inhibiting the previous one. This process is demanding and typically results in slower performance when switching tasks compared to repeating the same task.

Task inertia refers to the mental state of being in the mindset of doing a particular task, making it harder to switch to another task. When a new stimulus requiring a different task is presented, we experience this resistance to change.

To switch tasks internally, you need to:

1. Update or reconfigure mental representations for the new task

2. Hold multiple task sets in working memory

3. Suppress the now-irrelevant previous task

4. Activate the cognitive processes required for the new task

This switching process requires significant mental effort, leading to measurable "switch costs" in reaction time experiments. These costs are especially evident when switching between two well-practiced tasks, as the automatic nature of the previous task must be actively inhibited to perform the new task.

What is goal-directed attention? How is control implemented regarding anticipation? Reaction?

Goal-directed attention is attention that's deliberately focused toward achieving specific objectives, primarily implemented through the frontal lobe of the brain. Damage to this region impairs cognitive control and leads to reduced inhibition of irrelevant information.

Control implementation occurs through two primary mechanisms:

Anticipation (proactive):

• Control activity is anticipatory and preparatory

• Engaged when we know conflict or difficulty is coming

• Example: Being told you'll need to name the color in a Stroop task, so you prepare mentally before seeing the stimulus

• Maintains goals actively in working memory

• More effective but cognitively demanding

Reaction (reactive):

• Control is activated at the moment it's needed

• Engages after perceiving interference

• Example: Seeing a Stroop stimulus and then resolving the conflict

• Requires perceiving an element that triggers control

• Less cognitively demanding but less efficient

In the Stroop task example, anticipatory control might involve maintaining the goal "focus on color, ignore words" before seeing any stimuli, while reactive control would involve detecting the word-color conflict after seeing the stimulus and then resolving it.

What are dual modes of cognitive control? What is reactive control? What is proactive control? Explain the AXCPT task and what it means for these.

Dual modes of cognitive control refer to the two primary ways cognitive control is implemented: proactive and reactive control.

Reactive control is:

• Activated in the moment when control is needed

• Responds to interference after it appears

• Stimulus-dependent

• Less cognitively demanding but less efficient

• Example: Only remembering to shop when seeing a grocery list

Proactive control is:

• Planning and preparatory control engaged before control is needed

• Goal maintenance that anticipates challenges

• Stimulus-independent

• More cognitively demanding but more efficient

• Example: Maintaining shopping plans in mind throughout the day

The AXCPT (AX-Continuous Performance Task) measures these control modes:

• Participants see letter pairs (e.g., A followed by X)

• Rule: Respond "target" to X only when it follows A; respond "non-target" to all other pairs

• Different error patterns reveal control modes:

  • Proactive control users make more errors on AY trials (responding to Y after A) because they strongly prepared for X after seeing A

  • Reactive control users make more errors on BX trials (responding to X after B) because they react to X without fully considering the preceding letter

People with larger working memory capacity tend to use more proactive control, making fewer BX errors but responding more slowly on AY trials due to the detriment of preparing based on the A prime.

How do we increase control? What does it mean when there is high conflict with control? Low conflict? Why do we exert cognitive control?

We increase cognitive control in two primary ways:

1. By detecting conflict reactively: When we experience high conflict (like incongruent Stroop trials), the brain monitors this conflict, signals the need for control, and initiates mechanisms to suppress irrelevant information. High conflict means interference between goal-relevant tasks and competing responses, requiring increased control to resolve. Low conflict means minimal interference between tasks, requiring less cognitive control.

2. By learning to associate contexts with control demands: Studies show people can learn which contexts require more control. For example, when "dog" stimuli are 75% incongruent while "bird" stimuli are 25% incongruent, people automatically increase control when seeing dogs. This learning extends to new exemplars from the same category, showing we adapt control based on learned associations.

We exert cognitive control to achieve goals. Effective cognitive control predicts:

• Academic success

• Increased mental health

• Social competence

The brain implements optimal control to effectively work with demands, focusing more attention on situations it has learned are demanding. The prefrontal cortex plays a crucial role in maintaining task goals and implementing control strategies to overcome prepotent responses that would interfere with goal attainment.